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:: Ebiquity Research Group :: CSEE :: UMBC :: :: :: The Vision Pervasive Computing: a natural extension of the present human computing life style Using computing technologies will be as natural as using other non-computing technologies (e.g., pen, paper, and cups) Computing services will be something that is available anytime and anywhere.

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:: Ebiquity Research Group :: CSEE :: UMBC :: :: :: One Step Towards the Vision Context-aware systems: computer systems that can anticipate the needs of users and act in advance by “understanding” their context Systems know I am the speaker Systems know you are the audiences Systems know we are in a meeting …

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:: Ebiquity Research Group :: CSEE :: UMBC :: :: :: Research Issues Context Modeling & Reasoning How to build representations of context that can be processed and reasoned about by the computers Knowledge Maintenance & Sharing How to maintain consistent knowledge about the context and share that information with other systems User Privacy Protection How to give users the control of their situational information (e.g., information acquired by the hidden sensors)

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:: Ebiquity Research Group :: CSEE :: UMBC :: :: :: Key Uses of OWL (1) Use OWL to define ontologies of context people, devices, events, time, space etc. Use the ontology semantics of OWL to reason about context Deduce context knowledge that can’t be directly acquired from the sensors Detect inconsistent knowledge that results from imperfect sensing

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:: Ebiquity Research Group :: CSEE :: UMBC :: :: :: Key Uses of OWL (2) Use OWL (RDF/XML) as the KR language for knowledge sharing Knowledge sharing => minimizing the cost of and redundancy in context sensing Use OWL as a meta-language to define other languages that are used in context-aware systems Policy languages for privacy and security Content languages for agent communications

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:: Ebiquity Research Group :: CSEE :: UMBC :: :: :: Objectives Developing an agent architecture to support pervasive context-aware systems Provides ontologies for context modeling and reasoning Includes a logic inference engine to reason with contextual information and to detect and resolve inconsistent context knowledge Defines a policy language that users can use to control the use and the sharing of their context information

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:: Ebiquity Research Group :: CSEE :: UMBC :: :: :: An EasyMeeting Scenario Her agent informs the broker of her role and intentions + The broker tells her location to her agent A The projector agent wants to help Alice The projector agent asks slide show info. B The projector agent sets up the slides The broker informs the subscribed agents B

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:: Ebiquity Research Group :: CSEE :: UMBC :: :: :: The CoBrA Ontology Goal: it attempts to capture a set of common ontologies for describing People, places, devices, agents, services and non-computing objects in an intelligent meeting room environment The properties and relationships between these entities and the environment

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:: Ebiquity Research Group :: CSEE :: UMBC :: :: :: Spotting Error in Sensors Premise (static knowledge): R210 rdf:type AtomicPlace. ParkingLot-B rdf:type AtomicPlace. Premise (dynamic knowledge): Harry isLocatedIn R210. Harry isLocatedIn ParkingLot-B. Premise (domain knowledge): No person can be located in two different AtomicPlace during the same time interval. Conclusion: There is an error in the knowledge base.

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:: Ebiquity Research Group :: CSEE :: UMBC :: :: :: F-OWL (v0.3) F-OWL is an implementation of the OWL inference rules in Flora-2. Flora-2 is an F-Logic (Frame Logic) based language in XSB (Prolog). F-Logic is an object-oriented knowledge representation language. Similar to TRIPLE, F-OWL defines the ontology models in rules.

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:: Ebiquity Research Group :: CSEE :: UMBC :: :: :: More about F-OWL F-OWL is still under development. F-OWL v0.3 (as of today) supports a full RDF-S inference and limited OWL inference (OWL-Lite and some OWL Full). http://umbc.edu/~hchen4/fowl/

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:: Ebiquity Research Group :: CSEE :: UMBC :: :: :: Work In Progress Adopting some censuses ontologies for modeling time and space (e.g., DAML spatial & temporal ontology, Region Connection Calculus (RCC), Allen’s temporal interval calculus) Implementing a rule based inference engine to reason about the temporal and spatial relations that are associated context events Using REI, a security policy language based on deontic concepts, to develop a policy-based systems to protect user privacy

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:: Ebiquity Research Group :: CSEE :: UMBC :: :: :: Privacy Policy Use Case (1) The speaker doesn’t want others to know the specific room that he is in, but does want others to know that he is present on the school campus He defines the following policies: Can share my location with a granularity of ~1 km radius The broker: isLocated(UMBC) => Yes! isLocated(RM223) => I don’t know!

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:: Ebiquity Research Group :: CSEE :: UMBC :: :: :: Privacy Policy Use Case (2) The problem of inference! Knowing your phone + white pages => I know where you live Knowing your email address (.mil,.gov) => I know you works for the government The broker models the inference capability of other agents mayKnow(X, homeAdd(Y)) :- know(X,phoneNum(Y))

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:: :: Conclusions Semantic Web languages will play an important role in the future pervasive context- aware systems It provides a means for modeling context and reasoning about them. It allows independently developed agents to share context knowledge The Context Broker Architecture distinguishes itself from other frameworks in the use of Semantic Web technologies.